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7 Upload Challenge 1

7.1 Revenue by state

# CHALLENGE 1 Preparation ----
# 1.0 Load libraries ----
library(tidyverse)
library(readxl)
library(lubridate)
library(ggplot2)

# 2.0 Importing Files ----
bikes_tbl <- read_excel(path = "../../ds_data/01_bike_sales/01_raw_data/bikes.xlsx")
orderlines_tbl <- read_excel(path = "../../ds_data/01_bike_sales/01_raw_data/orderlines.xlsx")

#> New names:
#> * `` -> `...1`

bikeshops_tbl <- read_excel(path = "../../ds_data/01_bike_sales/01_raw_data/bikeshops.xlsx")

# Procedure Plan, data clean up ----
# Like in the exercise
# 3. Joining data ----
left_join(orderlines_tbl, bikes_tbl, by = c("product.id" = "bike.id"))

bike_orderlines_joined_2_tbl <- orderlines_tbl %>%
  left_join(bikes_tbl, by = c("product.id" = "bike.id")) %>%
  left_join(bikeshops_tbl, by = c("customer.id" = "bikeshop.id"))
bike_orderlines_joined_2_tbl

# 4. Wrangling data ----
bike_orderlines_wrangled_2_tbl <- bike_orderlines_joined_2_tbl %>%
  
  # 4.1 Add the total price (price * quantity)
  mutate(total.price = price * quantity) %>%
  
  # 4.3 Optional: Reorganize. Using select to grab or remove unnecessary columns
  # 4.3.1 by exact column name
  select(-...1, -gender, -category, -url, -model, -model.year, -frame.material, -weight, -name, -lat, -lng) %>%
  
  # 4.3.2 by a pattern
  select(-ends_with(".id")) %>%
  
  # 4.3.3 Actually we need the column "order.id". Let's bind it back to the data
  bind_cols(bike_orderlines_joined_2_tbl %>% select(order.id)) %>%
  
  # 4.3.4 Separate the location
  separate(col = location,
           into = c("city", "state"),
           sep = ", ",
           convert = T) %>%
  
  # 4.3.4 You can reorder the data by selecting the columns in your desired order.
  select(order.id, city, state,
         price, quantity, total.price,
         everything()) %>%
  
  # 4.4 Rename columns because we actually wanted underscores instead of the dots
  set_names(names(.) %>% str_replace_all("\\.", "_"))


# CHALLENGE 1 ----

# Challenge 1.1 Revenue by state ----
# Step 1 - Manipulate
sales_by_loc_tbl <- bike_orderlines_wrangled_2_tbl %>%
  select(state, total_price) %>%
  group_by(state) %>%
  summarize(sales = sum(total_price)) %>%
  mutate(sales_text = scales::dollar(sales, big.mark = ".",
                                     decimal.mark = ",",
                                     prefix = "",
                                     suffix = " €"))
sales_by_loc_tbl

# Step 2 - Visualize
sales_by_loc_tbl %>%
  ggplot(aes(x = state, y = sales)) +
  geom_col(fill = "#2DC6D6") +
  #geom_label(aes(label = sales_text)) +
  #geom_smooth(method = "lm", se = FALSE) +
  scale_y_continuous(labels = scales::dollar_format(big.mark = ".", 
                                                    decimal.mark = ",", 
                                                    prefix = "", 
                                                    suffix = " €")) +
  labs(
    title = "Revenue by state", 
    x = "", 
    y = "Revenue"
  ) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

7.2 Revenue by state and year

# Challenge 1.2 Revenue by state and year ----
# Step 1 - Manipulate
sales_by_loc_year_tbl <- bike_orderlines_wrangled_2_tbl %>%
  select(state, total_price, order_date) %>%
  mutate(year = year(order_date)) %>%
  group_by(state, year) %>%
  summarize(sales = sum(total_price)) %>%
  ungroup() %>%
  mutate(sales_text = scales::dollar(sales, big.mark = ".",
                                     decimal.mark = ",",
                                     prefix = "",
                                     suffix = " €"))

#> `summarise()` has grouped output by 'state'. You can override using the
#> `.groups` argument.

sales_by_loc_year_tbl

# Step 2 - Visualize
sales_by_loc_year_tbl %>%
  ggplot(aes(x = year, y = sales, fill = states)) +
  geom_col(fill = "#2DC6D6") +
  facet_wrap(~ state)+
  #geom_label(aes(label = sales_text)) +
  #geom_smooth(method = "lm", se = FALSE) +
  scale_y_continuous(labels = scales::dollar_format(big.mark = ".", 
                                                    decimal.mark = ",", 
                                                    prefix = "", 
                                                    suffix = " €")) +
  labs(
    title = "Revenue by year and state",
    fill = "State",
    y = "Revenue",
    x = "Year"
  )+
  theme(axis.text.x = element_text(angle = 45, hjust = 1))